Category Archives: Artificial Intelligence
It is safe to say that the closest thing next to human intelligence and abilities is artificial intelligence. Powered by its tools in machine learning, deep learning and neural network, there are so many things that existing artificial intelligence models are capable of. However do they dream or have psychedelic hallucinations like humans? Can the generative feature of deep neural networks experience dream like surrealism?
Neural networks are type of machine learning, focused on building trainable systems for pattern recognition and predictive modeling. Here the network is made up of layersthe higher the layer, the more precise the interpretation. Input data feed goes through all the layers, as the output of one layer is fed into the next layer. Just like neuron is the basic unit of the human brain, in a neural network, it is perceptron which forms the essential building block. A perceptron in a neural network accomplishes simple signal processing, and these are then connected into a large mesh network.
Generative Adversarial Network (GAN) is a type of neural network that was first introduced in 2014 by Ian Goodfellow. Its objective is to produce fake images that are as realistic as possible. GANs havedisrupted the development of fake images: deepfakes. The deep in deepfake is drawn from deep learning. To create deepfakes, neural networks are trained on multiple datasets. These dataset can be textual, audio-visual depending on the type of content we want to generate. With enough training, the neural networks will be able to create numerical representations the new content like a deepfake image. Next all we have to do is rewire the neural networks to map the image on to the target. Deepfake can also be created using autoencoders, which is a type of unsupervised neural network. In fact, in most of the deepfakes, autoencoders is the primary type of neural network used in their creation.
In 2015, a mysterious photo appeared onRedditshowing a monstrous mutant. This photo was later revealed to be a result of Google artificial neural network. Many pointed out that this inhumanly and scary appearing photo had striking resemblance to what one sees on psychedelic substances such as mushrooms or LSD.Basically, Google engineers decided that instead of asking the software to generate a specific image, they would simply feed it an arbitrary image and then ask it what it saw.
As per an abstract on Popular Science, Google used the artificial neural netowrk to amplify patterns it saw in pictures. Each artificial neural layer works on a different level of abstraction, meaning some picked up edges based on tiny levels of contrast, while others found shapes and colors. They ran this process to accentuate color and form, and then told the network to go buck wild, and keep accentuating anything it recognizes. In the lower levels of network, the results were similar to Van Gogh paintings: images with curving brush strokes, or images with Photoshop filters. After running these images through the higher levels, which recognize full images, like dogs, over and over, leaves transformed into birds and insects and mountain ranges transformed into pagodas and other disturbing hallucinating images.
Few years ago, Googles AI company DeepMindwas working on a new technology, which allows robots to dream in order to improve their rate of learning.
In a new article published in the scientific journalNeuroscience of Consciousness, researchers demonstrate how classic psychedelic drugs such as DMT, LSD, and psilocybin selectively change the function of serotonin receptors in the nervous system. And for this they gave virtual versions of the substances to neural network algorithms to see what happens.
Scientists from Imperial College London and the University of Geneva managed to recreate DMT hallucinations by tinkering around with powerful image-generating neural nets so that their usually-photorealistic outputs became distorted blurs. Surprisingly, the results were a close match to how people have described their DMT trips. As per Michael Schartner, a member of the International Brain Laboratory at Champalimaud Centre for the Unknown in Lisbon, The process of generating natural images with deep neural networks can be perturbed in visually similar ways and may offer mechanistic insights into its biological counterpart in addition to offering a tool to illustrate verbal reports of psychedelic experiences.
The objective behind this was to betteruncover the mechanismsbehind the trippy visions.
One basic difference between human brain and neural network is that our neurons communicate in multi-directional manner unlike feed forward mechanism of Googles neural network. Hence, what we see is a combination of visual data and our brains best interpretation of that data. This is also why our brain tends to fail in case of optical illusion. Further under the influence of drugs, our ability to perceive visual data is impaired, hence we tend to see psychedelic and morphed images.
While we have found answer to Do Androids Dream of Electric Sheep? by Philip K. Dick, an American sci-fi novelist; which is NO!, as artificial intelligence have bizarre dreams, we are yet to uncover answers about our dreams. Once we achieve that, we can program neural models to produce visual output or deepfakes as we expect. Besides, we may also apparently solve the mystery behind black box decisions.
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Does Artificial Intelligence Have Psychedelic Dreams and Hallucinations? - Analytics Insight
Global Healthcare Artificial Intelligence Report 2020-2027: Market is Expected to Reach $35,323.5 Million – Escalation of AI as a Medical Device -…
Dublin, Jan. 08, 2021 (GLOBE NEWSWIRE) -- The "Artificial intelligence in Healthcare Global Market - Forecast To 2027" report has been added to ResearchAndMarkets.com's offering.
Artificial intelligence in healthcare global market is expected to reach $35,323.5 million by 2027 growing at an exponential CAGR from 2020 to 2027 due to the gradual transition from volume to value-based healthcare
The surging need to accelerate and increase the efficiency of drug discovery and clinical trial processes, advancement of precision medicines, escalation of AI as a medical device, increasing prevalence of chronic, communicable diseases and escalating geriatric population and the increasing trend of acquisitions, collaborations, investments in the AI in healthcare market.
Artificial intelligence (AI) is the collection of computer programs or algorithms or software to make machines smarter and enable them to simulate human intelligence and perform various higher-order value-based tasks like visual perception, translation between languages, decision making and speech recognition.
The rapidly evolving vast and complex healthcare industry is slowly deploying AI solutions into its mainstream workflows to increase the productivity of various healthcare services efficiently without burdening the healthcare personnel, to streamline and optimize the various healthcare-associated administrative workflows, to mitigate the physician deficit and burnout issues effectively, to democratize the value-based healthcare services across the globe and to efficiently accelerate the drug discovery and development process.
Artificial intelligence in healthcare global market is classified based on the application, end-user and geography.
Based on the application, the market is segmented into Medical diagnosis, drug discovery, precision medicines, clinical trials, Healthcare Documentation management and others consisting of AI guided robotic surgical procedures and AI-enhanced medical device and pharmaceutical manufacturing processes.
The AI-powered Healthcare documentation management solutions segment accounted for the largest revenue in 2020 and is expected to grow at an exponential CAGR from 2020 to 2027. AI-enhanced Drug Discovery solutions segment is the fastest emerging segment, growing at an exponential CAGR from 2020 to 2027.
The artificial intelligence in healthcare global end-users market is grouped into Hospitals and Diagnostic Laboratories, Pharmaceutical companies, Research institutes and other end-users consisting of health insurance companies, medical device and pharmaceutical manufacturers and patients or individuals in the home-care settings.
Among these end users, Hospitals and Diagnostic Laboratories segment accounted for the largest revenue in 2020 and is expected to grow at an exponential CAGR during the forecasted period. Pharmaceutical companies segment is the fastest-growing segment, growing at an exponential CAGR from 2020 to 2027.
The artificial intelligence in healthcare global market by geography is segmented into North America, Europe, Asia-Pacific and the Rest of the world (RoW). North American region dominated the global artificial intelligence in healthcare market in 2020 and is expected to grow at an exponential CAGR from 2020 to 2027. The Asia-Pacific region is the fastest-growing region, growing at an exponential CAGR from 2020 to 2027.
The artificial intelligence in healthcare market is consolidated with the top five players occupying majority of the market share and the remaining minority share of the market being occupied by other players. Key Topics Covered:
1 Executive Summary
3 Market Analysis3.1 Introduction3.2 Market Segmentation3.3 Factors Influencing Market3.3.1 Drivers and Opportunities184.108.40.206 Aiabetting the Transition from Volume Based to Value Based Healthcare220.127.116.11 Acceleration and Increasing Efficiency of Drug Discovery and Clinical Trials18.104.22.168 Escalation of Artificial Intelligence as a Medical Device22.214.171.124 Advancement of Precision Medicines126.96.36.199 Acquisitions, Investments and Collaborations to Open An Array of Opportunities for the Market to Flourish188.8.131.52 Increasing Prevalence of Chronic, Communicable Diseases and Escalating Geriatric Population3.3.2 Restraints and Threats184.108.40.206 Data Privacy Issues220.127.116.11 Reliability Issues and Black Box Reasoning Challenges18.104.22.168 Ethical Issues and Increasing Concerns Over Human Workforce Replacement22.214.171.124 Requirement of Huge Investment for the Deployment of AI Solutions126.96.36.199 Lack of Interoperability Between AI Vendors3.4 Regulatory Affairs3.4.1 International Organization for Standardization3.4.2 Astm International Standards3.4.3 U.S.3.4.4 Canada3.4.5 Europe3.4.6 Japan3.4.7 China3.4.8 India3.5 Porter's Five Force Analysis3.6 Clinical Trials3.7 Funding Scenario3.8 Regional Analysis of AI Start-Ups3.9 Artificial Intelligence in Healthcare FDA Approval Analysis3.10 AI Leveraging Key Deal Analysis3.11 AI Enhanced Healthcare Products Pipeline3.12 Patent Trends3.13 Market Share Analysis by Major Players3.13.1 Artificial Intelligence in Healthcare Global Market Share Analysis3.14 Artificial Intelligence in Healthcare Company Comparison Table by Application, Sub-Category, Product/Technology and End-User
4 Artificial Intelligence in Healthcare Global Market, by Application4.1 Introduction4.2 Medical Diagnosis4.3 Drug Discovery4.4 Clinical Trials4.5 Precision Medicine4.6 Healthcare Documentation Management4.7 Other Application
5 Artificial Intelligence in Healthcare Global Market, by End-User5.1 Introduction5.2 Hospitals and Diagnostic Laboratories5.3 Pharmaceutical Companies5.4 Research Institutes5.5 Other End-Users
6 Regional Analysis
7 Competitive Landscape7.1 Introduction7.2 Partnerships7.3 Product Launch7.4 Collaboration7.5 Up-Gradation7.6 Adoption7.7 Product Approval7.8 Acquisition7.9 Others
8 Major Companies8.1 Alphabet Inc. (Google Deepmind, Verily Lifesciences)8.2 General Electric Company8.3 Intel Corporation8.4 International Business Machines Corporation (IBM Watson)8.5 Koninklijke Philips N.V.8.6 Medtronic Public Limited Company8.7 Microsoft Corporation8.8 Nuance Communications Inc.8.9 Nvidia Corporation8.10 Welltok Inc.
For more information about this report visit https://www.researchandmarkets.com/r/dxs2ch
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Caltech Professor to Explore Artificial Intelligence: How it Works and What it Means for the Future in Upcoming Event – Pasadena Now
Yisong YueCredit: Caltech
On Wednesday, January 13, at 5 p.m. Pacific Time,Yisong Yue, professor of computing and mathematical sciences in the Division of Engineering and Applied Science at Caltech, continues the 20202021 Watson Lecture season by exploring Artificial Intelligence: How it Works and What it Means for the Future.
Over the past decade, artificial intelligence (AI) and the massive amounts of data powering such systems have dramatically changed our world. And as both the technology and the way in which scientists and engineers handle it becomes more refined, the impact of AI in society will become more profound. In this lecture, Yue will explore the key principles powering the current revolution in AI, consider how cutting-edge AI techniques are transforming how research is done across science and engineering at Caltech, and examine what all of this means for the future of material design, robotics, and big data seismology, among other areas of investigation.
Yue will show how, where human intuition breaks down, AI can guide scientists in finding data-driven solutions to complex problems.
Yue, who joined the Caltech faculty as an assistant professor in 2014 and became a full professor in 2020, was previously a research scientist at Disney Research. Before that, he was a postdoctoral researcher in the machine learning department and the iLab at Carnegie Mellon University. He received his PhD from Cornell University and his BS from the University of Illinois at Urbana-Champaign.
Yues research interests lie primarily in the theory and application of statistical machine learning. He is interested in developing novel methods for both interactive and structured machine learning. In the past, his research has been applied to information retrieval, analyzing implicit human feedback, clinical therapy, data-driven animation, behavior analysis, sports analytics, experiment design for science, and policy learning in robotics, among other areas of inquiry.
This event is free and open to the public.Advance registrationis required. The lecture will begin at 5 p.m. and run approximately 45 minutes, followed by a live audience Q&A session with Yue. After the live webinar, the lecture (without Q&A) will be available for on-demand viewing onCaltechs YouTube channel.
Since 1922, The Earnest C. Watson Lectures have brought Caltechs most innovative scientific research to the public. The series is named for Earnest C. Watson, a professor of physics at Caltech from 1919 until 1959. Spotlighting a small selection of the pioneering research Caltechs professors are currently conducting, the Watson Lectures are geared toward a general audience as part of the Institutes ongoing commitment to benefiting the local community through education and outreach. Through a gift from the estate of Richard C. Biedebach, the lecture series has expanded to also highlight one assistant professors research each season.
Watson Lecturesare part of the Caltech Signature Lecture Series, presented by Caltech Public Programming, which offers a deep dive into the groundbreaking research and scientific breakthroughs at Caltech and JPL.
?? Register for the Zoom webinar
For more information, visithttps://events.caltech.edu/calendar/watson-lecture-2021-01.
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Artificial Intelligence Market Classification By Suppliers, Consumption, Application and Overview – KSU | The Sentinel Newspaper
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Artificial Intelligence Marketresearch report encompasses a far-reaching research on the current conditions of the industry, potential of the market in the present and the future prospects. By taking into account strategic profiling of key players in the industry, comprehensively analysing their core competencies, and their strategies such as new product launches, expansions, agreements, joint ventures, partnerships, and acquisitions, the report helps businesses improve their strategies to sell goods and services. This wide-ranging market research report is sure to help grow your business in several ways. Hence, the Artificial Intelligence Market report brings into the focus, the more important aspects of the market or industry.
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Major Market Key Players: Artificial Intelligence Market
The renowned players in artificial intelligence market are Welltok, Inc., Intel Corporation, Nvidia Corporation, Google Inc., IBM Corporation, Microsoft Corporation, General Vision, Enlitic, Inc., Next IT Corporation, iCarbonX, Amazon Web Services, Apple, Facebook Inc., Siemens, General Electric, Micron Technology, Samsung, Xillinx, Iteris, Atomwise, Inc., Lifegraph, Sense.ly, Inc., Zebra Medical Vision, Inc., Baidu, Inc., H2O ai, Enlitic, Inc. and Raven Industries.
Market Analysis: Artificial Intelligence Market
The Global Artificial Intelligence Market accounted for USD 16.14 billion in 2017 and is projected to grow at a CAGR of 37.3% the forecast period of 2018 to 2025. The upcoming market report contains data for historic years 2016, the base year of calculation is 2017 and the forecast period is 2018 to 2025.
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The Centre for Data Ethics and Innovation has published its review into bias in algorithmic decision-making; how to use algorithms to promote fairness, not undermine it. We wrote recently about the report's observations on good governance of AI. Here, we look at the report's recommendations around transparency of artificial intelligence and algorithmic decision-making used in the public sector (we use AI here as shorthand).
The need for transparency
The public sector makes decisions which can have significant impacts on private citizens, for example related to individual liberty or entitlement to essential public services. The report notes that there is increasing recognition of the opportunities offered through the use of data and AI in decision-making. Whether those decisions are made using AI or not, transparency continues to be important to ensure that:
However, the report identifies, in our view, three particular difficulties when trying to apply transparency to public sector use of AI.
First, the risks are different. As the report explains at length there is a risk of bias when using AI. For example, where groups of people within a subgroup is small, data used to make generalisations can result in disproportionately high error rates amongst minority groups. In many applications of predictive technologies, false positives may have limited impact on the individual. However in particularly sensitive areas, false negatives and positives both carry significant consequences, and biases may mean certain people are more likely to experience these negative effects. The risk of using AI can be particularly great for decisions made by public bodies given the significant impacts they can have on individuals and groups.
Second, the CDEI's interviews found that it is difficult to map how widespread algorithmic decision-making is in local government. Without transparency requirements it is more difficult to see when AI is used in the public sector which risks suggested intended opacity (see our previous article on widespread use by local councils of algorithmic decision-making here), how the risks are managed, or to understand how decisions are made.
Third, there are already several transparency requirements on the public sector (think publications of public sector internal decision-making guidance, or equality impact assessments) but public bodies may find it unclear how some of these should be applied in the context of AI (data protection is a notable exception given guidance by the Information Commissioner's Office).
What is transparency?
What transparency means depends on the context. Transparency doesnt necessarily mean publishing algorithms in their entirety. That is unlikely to improve understanding or trust in how they are used. And the report recognises that some citizens may make, rightly or wrongly, decisions based on what they believe the published algorithms means.
The report sets out useful requirements to bear in mind when considering what type of transparency is desirable:
Recommendation - transparency obligation
In order to give clarity to what is meant by transparency, and to improve it, the report recommends:
Government should place a mandatory transparency obligation on all public sector organisations using algorithms that have a significant influence [by affecting the outcome in a meaningful way] on significant decisions [i.e. that have a direct impact, most likely one that has an adverse legal impact or significantly affects] affecting individuals. Government should conduct a project to scope this obligation more precisely, and to pilot an approach to implement it, but it should require the proactive publication of information on how the decision to use an algorithm was made, the type of algorithm, how it is used in the overall decision-making process, and steps taken to ensure fair treatment of individuals.
Some exceptions will be required, such as where transparency risks compromising outcomes, intellectual property, or for security & defence.
Further clarifications to the obligation, such as the meaning of "significant decisions" will also be required. As a starting point, though, the report anticipates a mandatory transparency publication to include:
The report expects that identifying the right level of information on the AI is the most novel aspect. CDEI expect that other examples of transparency may be a useful reference, including the Government of Canadas Algorithmic Impact Assessment, a questionnaire designed to help organisations assess and mitigate the risks associated with deploying an automated decision system (and which we referred to in a recent post about global perspectives on regulating for algorithmic accountability).
A public register?
Falling short of an official recommendation, the CDEI also notes that the House of Lords Science and Technology Select Committee and the Law Society have both recently recommended that parts of the public sector should maintain a register of algorithms in development or use (these echo calls from others for such a register as part of a discussion on the UK's National Data Strategy). However, the report notes the complexity in achieving such a register and therefore concludes that "the starting point here is to set an overall transparency obligation, and for the government to decide on the best way to coordinate this as it considers implementation" with a potential register to be piloted in a specific part of the public sector.
Government is increasingly automating itself with the use of data and new technology tools, including AI. Evidence shows that the human rights of the poorest and most vulnerable are especially at risk in such contexts. A major issue with the development of new technologies by the UK government is a lack of transparency. The UN Special Rapporteur on Extreme Poverty and Human Rights, Philip Alston.
Artificial intelligence is one of the hottest buzzwords in legal technology today, but many people still dont fully understand what it is and how it can impact their day-to-day legal work.
According to Brookings Institution, artificial intelligence generally refers to machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment, and intention. In other words, artificial intelligence is technology capable of making decisions that generally require a human level of expertise. It helps people anticipate problems or deal with issues as they come up. (For example, heres how artificial intelligence greatly improves contract review.)
Recently, we sat down with Onits Vice President of Product Management, technology expert and patent holder Eric Robertson to cover the ins and outs of artificial intelligence in more detail. In this first installment of our new blog series, well discuss what it is and its three main hallmarks.
At the core of artificial intelligence and machine learning are algorithms, or sequences of instructions that solve specific problems. In machine learning, the learning algorithms create the rules for the software, instead of computer programmers inputting them, as is the case with more traditional forms of technology. Artificial intelligence can learn from new data without additional step-by-step instructions.
This independence is crucial to our ability to use computers for new, more complex tasks that exceed the manual programming limitations things like photo recognition apps for the visually impaired or translating pictures into speech. Even things we now take for granted, like Alexa and Siri, are prime examples of artificial intelligence technology that once seemed impossible. We already encounter in our day-to-day lives in numerous ways and that influence will continue to grow.
The excitement about this quickly evolving technology is understandable, mainly due to its impacts on data availability, computing power and innovation. The billions of devices connected to the internet generate large amounts of data and lower the cost of mass data storage. Machine learning can use all this data to train learning algorithms and accelerate the development of new rules for performing increasingly complex tasks. Furthermore, we can now process enormous amounts of data around machine learning. All of this is driving innovation, which has recently become a rallying cry among savvy legal departments worldwide.
Once you understand the basics of artificial intelligence, its also helpful to be familiar with the different types of learning that make it up.
The first is supervised learning, where a learning algorithm is given labeled data in order to generate a desired output. For example, if the software is given a picture of dogs labeled dogs, the algorithm will identify rules to classify pictures of dogs in the future.
The second is unsupervised learning, where the data input is unlabeled and the algorithm is asked to identify patterns on its own. A typical instance of unsupervised learning is when the algorithm behind an eCommerce site identifies similar items often bought by a consumer.
Finally, theres the scenario where the algorithm interacts with a dynamic environment that provides both positive feedback (rewards) and negative feedback. An example of this would be a self-driving car where, if the driver stays within the lane, the software will receive points in order to reinforce that learning and reminders to stay in that lane.
Even after understanding the basic elements and learning models of artificial intelligence, the question often arises as to what the real essence of artificial intelligence is. The Brookings Institution boils the answer down to three main qualities:
In the next installment of our blog series, well discuss the benefits AI is already bringing to legal departments. We hope youll join us.
The United States received huge rewards from the last influx of benefits development, getting home to a portion of the worlds best tech organizations, for example, Amazon, Apple, Facebook, Google, Intel, and Microsoft. Then, numerous parts of the world, including the European Union, paid an economic price remaining uninvolved. Perceiving that missing the next wave of developmentfor this situation, AIwould be comparatively dangerous, numerous countries are making a move to guarantee they play a large role in the next digital transformation of the global economy.
As of now, the worlds digital goliaths, for example, Amazon, Facebook, Google, Intel, Microsoft, and Alibabaoverwhelm Europes AI landscape without offering a lot, assuming any, financial advantage for European nations and organizations. Without local rivalry, they can work without making significant investments locally, without making occupations on the continent, and frequently without paying much tax. At the point when European nations push back, it leads to cross-border tensions.
Until now, the EU has requested little from the digital giants besides essential compliance with data-protection laws, platform business rules, and other related guidelines. In any case, the EU is progressively awakening to the fact that it is one of the worlds greatest digital markets, appreciates significant negotiating power, and that it should utilize this power for its benefits
Albeit multilateral negotiations will proceed, the EU appears liable to take one-sided measures to guarantee a level battleground. The EUs policy climate has changed an incredible arrangement lately, with the presentation of the General Data Protection Regulation (GDPR) and rulings against digital giants, for example, Facebook and Google. A few different changes are in the pipeline, including the recently unveiled data strategy by the European Commission. The EUs mentality is progressively isolated, with policymakers calling for EU digital sovereignty and respect for EU values for AI.
The AI startups in Europe are far greater in number than youd most likely give them acknowledgment for. While Europe isnt popular for startups, the numbers are very reassuring. Of the 2,451 AI new companies that Statista reports as of 2018, 675 have a place with European nations (the UK alone has 245).
Digital goliaths need to perceive the essential opportunity introduced by the EU. Its not just about size or buying power; the EU is quite possibly the most refined and differentiated AI markets, particularly for industrial applications. The EU gives opportunities to create and train algorithms for a few enterprises, and it will be outlandish for any digital giant to guarantee it has a worldwide contribution if it doesnt have access to European business markets, information, and Europe-trained AI applications. Besides, by supporting the EUs huge talent pools, IT organizations can supercharge their AI teams.
Europe doesnt need to fight each and every AI conflict to win the war. There are regions where Europe could be facing a losing conflict as well.
Yet, there are plenty of fronts on which top AI organizations in Europe may handily be clear victors. It, for example, as of now has an edge in B2B and industrial robotics. That, and a pan-Europe network of AI-based advancement hubs could be more than what China or the USA might deal with.
AI4EU is an on-demand AI platform. It pools together 80 partners across 21 nations. Subsidized with Euro 20 million, AI4EU is a project that will run for a very long time. Its activities will zero in on the utilization of AI for healthcare, agriculture, robotics, and IoT, in addition to other things. Artificial intelligence in healthcare in Europe is quite promising, and when worked with farming, it could change various things.
Essentially, it looks to make the advantages of AU available to all. Todays AI guidelines will play a significant role in molding the EUs business environment of tomorrow. Just consenting to regulations wont set up the digital giants for success; picking up the upper hand requires understanding Europes nuances and assisting with shaping future regulations. Besides, numerous global organizations have just applied the EUs GDPR prerequisites to their overall tasks, strengthening the requirement for the digital giants to acquire a seat at the table in Brussels, where future policies will be fashioned.
In certain regions, nonetheless, the competition to create or embrace AI is definitely not a lose-lose situation. Improvements of AI science, especially at colleges, can and do spread all through the world, consequently helping the whole AI ecosystem. Also, numerous AI headways, especially those focussed on the environment, health, and education can profit all nations. For instance, the advancement of AI frameworks that can discover diseases quicker and more precisely than clinicians, or produce new medical treatments, offers conceivably worldwide advantages.
CARY Looking to speed up artificial intelligence incorporation into data analytics and cloud computing as well asdevices such as wearables, SAS on Thursday disclosed the acquisition of UK-based Boemska.
The company already works with SAS, specializing in low-code/no-code application deployment and analytic workload management for the SAS technology platform, SAS noted. Boemska isa well-established SAS technology partner whose global customers include SAS customers in financial services, health care and travel, SAS added.
Financial terms were not disclosed.
The news came on the same day that SAS announced the promotion of veteran executive Bryan Harris to chief technology officer.
SAS promotes current executive to Chief Technology Officer
SAS is on a journey to enable AI and analytics for everyone, everywhere, Harris said in a statement about the acquisition.We have not only transformed the way in which we build and deliver software with recent SAS Viya updates and a cloud partnership with Microsoft, but also the speed and manner with which customers can achieve value. SAS is recognized as a leading provider of analytics for enterpriseapplications. Boemskas technology puts SAS closer to where decisions are made, and available in cloud marketplaces for applications developers.
Boemska has an R&D center in Serbia.
SAS noted two major technology strengths that the deal adds to its portfolio:
A next-generation, cloud-native capability enabling portability of SAS and open-source models into mobileand enterprise applications. This enables development and execution of models and decisions using lowcode and no-code technologies for performing specific tasks such as anticipating fraud, decision makingrelated to a medical event, identifying a manufacturing defect and more. An enterprise workload management tool that facilitates migration of scale-out analytics to the cloud in acost-efficient way while ensuring that analytic workloads on clouds such as Microsoft Azure remain rightsized and always optimized. This brings unparalleled visibility to SAS workloads running on shared multiuser environments and empowers customers to confidently execute their cloud migration strategy.
Were excited to join the SAS family and help shift customers to the cloud in a cost-effective yet powerful manner, said Nikola Markovic, Boemska Chief Technology Officer, in a statement. We look forward to collaboratively delivering a portable, small-footprint runtime for analytics and models while improving the ability to migrate to the cloud.
UF photo shows Assistant Professor Yiannis Ampatzidis.
What was once the future of farming is happening today at the University of Florida/IFAS.
Scott Angle, Vice President for Agriculture and Natural Resources, explains how UF/IFAS is using artificial intelligence (AI) to help producers be more efficient in their farming operations.
Its a fascinating issue right now. The University of Florida wants to move into the top 5 for public universities. Theyre ranked sixth right now. They believe that artificial intelligence (AI) will be the tool to do that, as well as, Florida seems to be a great place to become the center of the world for artificial intelligence, Angle said.
According to the UF/IFAS blog, artificial intelligence is the ability of a computer system to recognize patterns, understand language, learn from experience, solve problems and perform complex tasks. Its also described as the ability of a machine to think like a human but do it faster and more efficiently.
For farmers who care about every plant and tend to every animal on their farms, AI allows growers to compute millions of variables and coordinate vast amounts of data instantly and accurately.
Particularly in Florida, where there are many opportunities for artificial intelligence to replace labor and to scout out plant diseases, weed infestations, better weather predictions; these are all things AI can do, Angle said.
Agriculture, unfortunately, but now it becomes an opportunity, is behind the curve on artificial intelligence. Its a technology thats moving very quickly. We want to make sure that IFAS and the University of Florida is that organization that begins to move AI into agriculture much more quickly than it has been.
In the Animal Sciences Department, Albert De Vries team uses AI to get more accurate profiles of individual cattle, measuring their phenotypes to aid in breeding and using their genetic makeup to improve feeding efficiency.
In citrus, Yiannis Ampatzidis and his research team use AI-based software to analyze and visualize data collected from unmanned aerial vehicles (UAVs). UAVs can take images of thousands of plants and upload to software that analyzes the data to access plant qualities, quantities and growth factors.
In peanuts, Diane Rowland, Agronomy Department Chair, has developed a method using hyperspectral imaging and AI to determine peanut seed quality through the hull. This allows peanut producers to select mature seeds with greater accuracy and less expenditure of time and labor.
In weed research, scientist Nathan Boyd and precision agriculture specialist Arnold Schumann utilize AI to identify weeds in the field and distinguish them from crops. This allows herbicides to be applied only to the weeds, resulting in less spray damaged plants and reduced pesticide use.
Weve got people already working on things like, how do count citrus trees in a grove? Or how do you scout for new diseases that have just entered a field and may not be obvious from the roadside. Weve got lots of people working on this. Theres just so many opportunities that we feel its almost unlimited at this point, Angle said.
Theres dozens or hundreds of areas where artificial intelligence can play a role; all the way from Extension in providing more accurate information to weather forecasting to disease scouting; things that are often done through very laborious and sometimes not very accurate processes. Artificial intelligence and having the computer to do that takes away a lot of that uncertainty. It could just make us all better farmers.
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AI: The Future of Farming is Happening Today - Southeast AgNet
What does Integration of Artificial Intelligence and Advanced Analytics mean in Business? – Analytics Insight
What does Integration of Artificial Intelligence and Advanced Analytics mean in Business?
Disruptive technologies like artificial intelligence (AI) and advanced analytics have had a transformational impact on the finance industry. They are also changing the way enterprises interact with their clients and run their organizations. The emergence and rapid growth of these technologies helped companies enhance their processes and operations.
While data analytics refers to drawing insights from raw data, advanced analytics help collate previously untapped data sources, especially the unstructured data and data from the intelligent edge, to garner analytical insights. Meanwhile, artificial intelligence replicates behaviors that are generally associated with human intelligence. These include learning, reasoning, problem-solving, planning, perception, and manipulation. Some latest iterations of AI, like generative AI, can also create creative artwork, music, and more. Though these technologies sound diverse, their synergy would bring tremendous innovation across several industries. When powered by AI, advanced analytics algorithms can offer additional performance over other analytics techniques.
World Economic Forum states that the COVID-19 crisis provided a chance for advanced analytics and AI-based techniques to augment decision-making among business leaders too.
In a study conducted by Forrester Consulting on behalf of Intel, 98% of respondents believe that analytics is crucial to driving business priorities. Yet, fewer than 40% of workloads are leveraging advanced analytics or artificial intelligence. For instance, according to Deloitte Insights, only 70% of all financial services firms use machine learning to predict cash flow events, fine-tune credit scores, and detect fraud.
Advanced analytics and artificial intelligence are emerging favorites in the finance sector as they help firms authenticate customers, improve customer experience, and reduce the cost of maintaining acceptable levels of fraud risk, particularly in digital channels.As finance firms race inch to disruption, the velocity of fraud attacks and threats also increases. The amalgamation of these technologies helps mitigate such threats before there is any severe damage, thus increasing compliance. This is achieved by assessing risks, identifying potential suspicious activities, preventing fraudulent transactions, and more. Since AI powered analytical algorithms are adept at pattern recognition and processing large quantities of data, it is key to improving fraud detection rates. For customers, they can help authenticate any financial services they may be using and issue alert the customer if something is wrong.
This fraud detection capability is also helpful for brand marketers to distinguish successful campaigns and avoid wasteful spending. Boston Consulting Group has observed that consumer packaged goods (CPG) companies can boost more than 10% of their revenue growth through enhanced predictive demand forecasting, relevant local assortments, personalized consumer services, and experiences, optimized marketing and promotion ROI, and faster innovation cycles; all via the said technologies.
While factors like data silos, fear of missing out on the race to digital transformation and agility have influenced companies to rely on data-driven insights, they must leverage advanced analytics and artificial intelligence, to stay relevant in the market. In its September 2017 article, titledHow Big Consumer Companies Can Fight Back, Boston Consulting Group also mentions that these technologies top industry players can use them to transform their data into valuable insights. In other words, it can augment an enterprises ability to execute data-intensive workloads and, at the same time, keep the HPC environment adaptable, responsive, and cost-effective.
However, there are many difficulties faced by companies when adopting them too. As per a research survey by Ericsson IndustryLab, 91% of organizations surveyed reported facing problems in each of three categories of challenges studied, including technology, organizational, and company culture and people. It is true that artificial intelligence and advanced analytics tools allowed navigation and the re-imagining of all aspects of business operations, and the COVID-19 pandemic expedited their adoption. However, despite beingarguably the most powerful general-purpose technologies, companies must recognize potential, use cases, and strategize the right action plans to accelerate their artificial intelligence and advanced analytics undertakings.
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